Catálogo de Investigaciones 2020-2021

120 FACULTAD Bipartite networks of cancer genes associated with specific drugs using pharmacogenomics The study of the genome-wide activity of the genes (i.e. the expression of the genes or their genetic variation) may be related to differences in the effects of drugs. Pharmacogenomics allows better mapping of the targets of cancer drugs and potential interacting secondary agents, but there are many drugs whose mechanisms of action have not been fully deciphered [1]. Complex diseases such as cancer, with hundreds of clinically approved drugs could benefit from integrating these data in clinical trials and drug development [2, 3]. The study of these drug-targets to better understand their mechanism of action could lead to possible new treatments or an improvement of existing ones by overcoming drug resistance [4]. This study comprised a large-scale screening analysis to find new associations between chemical substances (non-FDA-approved cancer drug) and human genes using transcriptomic profiling [6]. The results showed 327 genes and 2733 drugs have 6183 positive correlations, across the sixty cancer cell lines (using a threshold of Pearson coefficient r < 0.60 and p-value ≤ 0.05). Berral-Gonzalez A, Arroyo MM, Alonso-Lopez D et al. Bipartite networks of cancer genes associated with specific drugs using pharmacogenomics. F1000 Research 2021, 10(ISCB Comm J):161 doi: 10.7490/f1000research.1118512.1 dra. mónica m. arroyo cabán departamento de química, pucpr bioinformatics and functional genomics group, cancer research center (cic-imbcc, csic/usal/ibsal), consejo superior de investigaciones científicas (csic) and university of salamanca (usal), 37007 salamanca, españa alberto berral-gonzález santiago bueno-fortes javier de las rivas bioinformatics and functional genomics group, cancer research center (cic-imbcc, csic/usal/ibsal), consejo superior de investigaciones científicas (csic) and university of salamanca (usal), 37007 salamanca, españa pharmacogenomics bipartitite nerworks cancer bioinformatics gene-drug targets

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